Formulation and manipulation of databases of analyte and associated values

ABSTRACT

The present invention relates to methods of formulating analyte data databases, the databases themselves, and methods of manipulating the same. In one aspect the present invention includes the formulation of analyte data points, derived data, and data attributes databases comprising data points collected using an analyte monitoring device capable of frequent monitoring of analyte concentrations or amounts. Such data points may comprise acquired data (e.g., values corresponding to analyte concentrations or amounts as measured by said analyte monitoring device). These data points are then associated with one or more relevant data attributes. The resulting databases may be manipulated to determine relationships among the components of the database.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is related to U.S. Provisional Patent Application Ser.No. 60/226,205, filed 18 Aug. 2000, from which priority is claimed under35 USC §119(e)(1), and which application is incorporated herein byreference in its entirety.

TECHNICAL FIELD

The present invention relates to the formulation of analyte data points,derived data, and data attributes into one or more databases, thedatabases themselves, and the manipulation of those databases to produceuseful information regarding factors correlated with analyte data.

BACKGROUND OF THE INVENTION

Previously, measurement of analyte levels in an individual wasinconvenient and time consuming. For example, monitoring blood glucoselevels in a diabetic formerly required a diabetic subject to obtain ablood sample, test that sample using e.g., a HemoCue® (Aktiebolaget Leo,Helsingborg, Sweden) clinical analyzer. Such methods typically require afinger-stick for each measurement. As a result, testing of blood glucoselevels was seldom performed more frequently than 2 times per day. Suchtesting does not provide a complete picture of the fluctuations ofglucose levels. In addition, unless the subject manually recorded theblood glucose level measured, that information was preserved only in thesubject's memory. Further information regarding other factors that couldaffect the user's blood glucose levels, such as food intake, physicalactivity, etc., were similarly lost. Written records were reliable onlyto the extent the subject was diligent about recording informationaccurately and consistency. Hence, formulation of a database comprising(1) data points (e.g., measured analyte levels) and (2) informationassociated with each data point (e.g., the date and time of themeasurement; activities affecting analyte levels such as food or waterintake or physical exertion; and drug administration), was previouslydifficult. Further, even if obtainable, the accuracy and precision ofsuch databases was suspect.

The present invention provides a novel method of formulating analytedata databases, analyte data databases themselves, and methods ofmanipulating the analyte data databases to produce useful information,e.g., for analyzing factors suspected of affecting analyte levels andinvestigating the efficacy of drug action for a large number ofexperimental subjects.

SUMMARY OF THE INVENTION

The present invention relates to methods of formulating analyte datadatabases (comprising, analyte data points, derived data, and dataattributes), the analyte data databases themselves, and to methods formanipulating and analyzing the analyte data databases.

In one aspect the present invention relates to methods of formulatingone or more analyte data databases (comprising, analyte data points,derived data, and data attributes). The databases may compriseinformation obtained for one or more analytes. In one embodiment of themethods, analyte measurement values are collected from one or moresubject using an analyte monitoring device for each subject. The analytemonitoring device may comprise a transdermal sampling device. Typically,the analyte monitoring device is capable of providing frequent analytemeasurement values, wherein the analyte measurement values compriseacquired data points that are specifically related to analyte amount orconcentration in the subject. These data points may be acquired atselected time intervals. One or more analyte data databases are thenformulated by associating each of the data points with one or more dataattributes.

In a further embodiment, the data points comprise derived datadetermined from one or more acquired data points and the derived dataare associated with the data points from which they are derived.Further, the derived data may be associated with one or more dataattributes.

Analyte measurement values used in these methods may be collected from asingle individual or from more than one individual. An analytemonitoring device is used by each individual during the course ofobtaining the analyte measurement values. The analyte measurement values(as well as data points, derived data, and data attributes) may becompiled into one or more analyte data databases. When multipledatabases are formulated, the data points collected from a singleindividual are typically associated with one or more relevant dataattributes. The data from each individual is then compiled into a large,communal database (e.g., a data warehouse). Further, relationships maybe established between the data points, derived data, and dataattributes from different individuals. In addition, population databasesmay be formulated that comprise compilations of derived data from morethan one individual (e.g., mean analyte measurement values across aselected population) as well as associated data attributes. Suchpopulation databases may be used for comparisons between differentpopulations (e.g., males/females, different races, different agegroupings, etc.).

In one embodiment of the present invention the analyte is a biologicalanalyte, e.g., glucose. The data points may, for example, be signalmeasurements related to the amount or concentration of the analyte. Inthis case, analyte amount or concentration may itself be a derived datapoint. The analyte monitoring device may be capable of detecting one ormore analyte. Alternatively, more than one monitoring device may be usedto generate data for use in formulating the analyte data databases ofthe present invention. In one aspect, the analyte is at least glucoseand the analyte monitoring device comprises a glucose monitoring device.A number of glucose monitoring devices may be used in the practice ofthe present invention. In a preferred embodiment, the glucose monitoringdevice comprises a transdermal sampling device, a sensing device, adisplay, and means to provide an audible alert when glucose levels in asubject being monitored are outside of a predetermined range. Thesensing device may, for example, comprise electrochemical devices,optical, chemical devices, and combinations thereof. In one aspect thesensing device comprises an electrochemical sensor and the acquired datapoints comprise electrochemical signals.

Data attributes include, but are not limited to, the following:chronological information, user perspiration levels, device operatingtemperature, missed measurements; skipped measurements, user bodytemperature, user skin conductance, environmental variables, alarmevents, activity codes, total excursion, mean value, statisticalfunction, subject code, demographic information, physicalcharacteristics, and disease-associated characteristics.

The present invention also comprises an analyte data database formulatedby the above method. For example, an analyte data database may beformulated from data points collected using an analyte monitoringdevice, the analyte monitoring device (i) comprising a transdermalsampling device, and (ii) providing frequent analyte measurement values.The analyte measurement values may comprise data points that arespecifically related to analyte amount or concentration. In thedatabase, the data points are associated with one or more relevant dataattributes.

The present invention also includes methods for manipulating one or moreanalyte data database. Such methods of manipulating the databases of thepresent invention are described in greater detail herein below. Forexample, one such method comprises one or more analyte data database ofthe present invention and manipulating the data points via theattributes associated with the data points to determine relationshipsbetween the data points and the attributes. Alternatively, or inaddition to the previous method, using one or more analyte datadatabases of the present invention, the attributes may be manipulatedvia the data points associated with the attributes to determinerelationships between the attributes and the data points.

These and other embodiments of the present invention will readily occurto those of ordinary skill in the art in view of the disclosure herein.

BRIEF DESCRIPTION OF THE FIGURES

FIGS. 1A, 1B, 2, and 3 provide examples of tabular databases of thepresent invention. FIG. 1B is a continuation of FIG. 1A. The dataappearing in FIGS. 1A/1B and 2 was collected from a single individualover the course of a single data collection run using a GlucoWatchbiographer. FIG. 1A/1B is a tabulation of unanalyzed data collectedduring the first one hour, five minutes of the data collection run, andprior to calibration of the biographer device. FIG. 2 is a tabulation ofunanalyzed data obtained over a 20-minute period during the same run,but after calibration of the biographer device. FIG. 3 is a tabulationof analyzed data derived from the data points collected during this run.In particular, the highlighted information in row 4 of FIG. 3 wasderived from the data points appearing in FIG. 2.

FIG. 4 presents data concerning average minimum temperature during eachbiographer measurement cycle vs. reference blood glucose.

FIG. 5 presents data for average skin conductivity reading vs. bloodglucose range.

FIG. 6 presents data concerning percentage of skin conductivity readingsindicating perspiration vs. blood glucose range.

FIG. 7 presents an illustration of a representative stand-alone computersystem, PDA or PPC interfaced with a glucose monitor system.

FIG. 8 presents an illustration of a representative networked computersystem, PDA or PPC suitable for performing the methods of the presentinvention.

FIG. 9 presents a schematic of an exploded view of exemplary componentscomprising one embodiment of an AutoSensor for use in a monitoringsystem.

DETAILED DESCRIPTION OF THE INVENTION

The practice of the present invention employs, unless otherwiseindicated, conventional methods of database storage and manipulation,within the skill-of the art. Such techniques are explained fully in theliterature. See, e.g., Numerical Mathematical Analysis, Third Edition,by J. B. Scarborough, 1955, John Hopkins Press, publisher; SystemAnalysis and Design Methods, by Jeffrey L. Whitten, et al., FourthEdition, 1997, Richard D. Irwin, publisher; Modern Database Management,by Fred R. McFadden, et al., Fifth Edition, 1999, Addison-Wesley Pub.Co., publisher; Modern System Analysis and Design, by Jeffery A. Hoffer,et al., Second Edition, 1998, Addison-Wesley Pub. Co., publisher; DataProcessing: Fundamentals, Design, and Implementation, by David M.Kroenke, Seventh Edition, 2000, Prentice Hall, publisher; Case Method:Entity Relationship Modelling (Computer Aided Systems Engineering), byRichard Barker, 1990, Addison-Wesley Pub Co., publisher.

All publications, patents and patent applications cited herein, whethersupra or infra, are hereby incorporated by reference in their entirety.

1. Definitions

Before describing the present invention in detail, it is to beunderstood that this invention is not limited to particular compositionsor biological systems as such may, of course, vary. It is also to beunderstood that the terminology used herein is for the purpose ofdescribing particular embodiments only, and is not intended to belimiting. Abused in this specification and the appended claims, thesingular forms “a”, “an” and “the” include plural referents unless thecontent clearly dictates otherwise. Thus, for example, reference to “areservoir” includes a combination of two or more such reservoirs,reference to “an analyte” includes mixtures of analytes, and the like.

Unless defined otherwise, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which the invention pertains. Although any methods andmaterials similar or equivalent to those described herein can be used inthe practice for testing of the present invention, the preferredmaterials and methods are described herein.

In describing and claiming the present invention, the followingterminology is used in accordance with the definitions set out below.

The term “microprocessor” refers to a computer processor contained on anintegrated circuit chip, such a processor may also include memory andassociated circuits. A microprocessor may further comprise programmedinstructions to execute or control selected functions, computationalmethods, switching, etc. Microprocessors and associated devices arecommercially available from a number of sources, including, but notlimited to, Cypress Semiconductor Corporation, San Jose, Calif.; IBMCorporation, White Plains, New York; Applied Microsystems Corporation,Redmond, Wash.; Intel Corporation, Chandler, Ariz.; and, NationalSemiconductor, Santa Clara, Calif.

The terms “analyte” and “target analyte” are used to denote anyphysiological analyte of interest that is a specific substance orcomponent that is being detected and/or measured in a chemical,physical, enzymatic, or optical analysis. A detectable signal (e.g., achemical signal or electrochemical signal) can be obtained, eitherdirectly or indirectly, from such an analyte or derivatives thereofFurthermore, the terms “analyte” and “substance” are usedinterchangeably herein, and are intended to have the same meaning, andthus encompass any substance of interest. In preferred embodiments, theanalyte is a physiological analyte of interest, for example, glucose, ora chemical that has a physiological action, for example, a drug orpharmacological agent.

A “sampling device, “sampling mechanism” or “sampling system” refers toany device and/or associated method for obtaining a sample from abiological system for the purpose of determining the concentration of ananalyte of interest. Such “biological systems” include any biologicalsystem from which the analyte of interest can be extracted, including,but not limited to, blood, interstitial fluid, perspiration and tears.Further, a “biological system” includes both living and artificiallymaintained systems. The term “sampling” mechanism refers to extractionof a substance from the biological system, generally across a membranesuch as the stratum corneum or mucosal membranes, wherein said samplingis invasive, minimally invasive, semi-invasive or non-invasive. Themembrane can be natural or artificial, and can be of plant or animalnature, such as natural or artificial skin, blood vessel tissue,intestinal tissue, and the like. Typically, the sampling mechanism is inoperative contact with a “reservoir,” or “collection reservoir,” whereinthe sampling mechanism is used for extracting the analyte from thebiological system into the reservoir to obtain the analyte in thereservoir. Non-limiting examples of sampling techniques includeiontophoresis, sonophoresis (see, e.g., International Publication No. WO91/12772, published 5 Sep. 1991; U.S. Pat. No. 5,636,632), suction,electroporation, thermal poration, passive diffusion (see, e.g.,International Publication Nos.: WO 97/38126 (published 16 Oct. 1997); WO97/42888, WO 97/42886, WO 97/42885, and WO 97/42882 (all published 20Nov. 1997); and WO 97/43962 (published 27 Nov. 1997)), microfine(miniature) lances or cannulas, biolistic (e.g., using particlesaccelerated to high speeds), subcutaneous implants or insertions, andlaser devices (see, e.g., Jacques et al. (1978) J. Invest. Dermatology88:88-93; International Publication WO 99/44507, published 1999 Sep. 10;International Publication WO 99/44638, published 1999 Sep. 10; andInternational Publication WO 99/40848, published 1999 Aug. 19).Iontophoretic sampling devices are described, for example, inInternational Publication No. WO 97/24059, published 10 Jul. 1997;European Patent Application EP 0942 278, published 15 Sep. 1999;International Publication No. WO 96/00110, published 4 Jan. 1996;International Publication No. WO 97/10499, published 2 Mar. 1997; U.S.Pat. Nos. 5,279,543; 5,362,307; 5,730,714; 5,771,890; 5,989,409;5,735,273; 5,827,183; 5,954,685 and 6,023,629, all of which are hereinincorporated by reference in their entireties. Further, a polymericmembrane may be used at, for example, the electrode surface to block orinhibit access of interfering species to the reactive surface of theelectrode.

The term “physiological fluid” refers to any desired fluid to besampled, and includes, but is not limited to, blood, cerebrospinalfluid, interstitial fluid, semen, sweat, saliva, urine and the like.

The term “artificial membrane” or “artificial surface,” refers to, forexample, a polymeric membrane, or an aggregation of cells of monolayerthickness or greater which are grown or cultured in vivo or in vitro,wherein said membrane or surface functions as a tissue of an organismbut is not actually derived, or excised, from a pre-existing source orhost.

A “monitoring system” or “analyte monitoring device” refer to a systemuseful for obtaining frequent measurements of a physiological analytepresent in a biological system. Such a device is useful, for example,for monitoring the amount or concentration of an analyte in a subject.Such a system may comprise, but is not limited to, a sampling mechanism,a sensing mechanism, and a microprocessor mechanism in operativecommunication with the sampling mechanism and the sensing mechanism.Such a device typically provides frequent measurement or determinationof analyte amount or concentration in the subject and provides an alertor alerts when levels of the analyte being monitored fall outside of apredetermined range. Such devices may comprise durable and consumable(or disposable) elements. The term “glucose monitoring device” refers toa device for monitoring the amount or concentration of glucose in asubject. Such a device typically provides a frequent measurement ordetermination of glucose amount or concentration in the subject andprovides an alert or alerts when glucose levels fall outside of apredetermined range. One such exemplary glucose monitoring device is theGlucoWatch biographer available from Cygnus, Inc., Redwood City, Calif.,US. The GlucoWatch biographer comprises two primary elements, a durableelement (comprising a watch-type housing, circuitry, display element,microprocessor element, electrical connector elements, and may furthercomprise a power supply) and a consumable, or disposable, element (e.g.,an AutoSensor component involved in sampling and signal detection, see,for example, WO 99/58190, published 18 Nov. 1999). This and similardevices is described, for example, in the following publications:Tamada, et al., (1999) JAMA 282:1839-1844; U.S. Pat. No. 5,771,890,issued 30 Jun. 1998; U.S. Pat. No. 5, 735,273, issued 7 Apr. 1998; U.S.Pat. No. 5,827,183, issued 27 Oct. 1998; U.S. Pat. No. 5,954,685, issued21 Sep. 1999; U.S. Pat. No. 5,989,409, issued 23 Nov. 1999; U.S. Pat.No. 6,023,629, issued 8 Feb. 2000; EP Patent Application EP 0 942 278A2, published 15 Sep. 1999; PCT International Application WO 96/001100,published 4 Jan. 1996; PCT International Application WO 99/58190,published 18 Nov. 1999. The GlucoWatch biographer provides a device forfrequent sampling of glucose from a subject the application of lowintensity electric fields across the skin (iontophoresis) to enhance thetransport of glucose from body tissues to a sampling chamber. Inaddition, when the concentration or amount of glucose has beendetermined to be outside of a predetermined range of values theGlucoWatch biographer produces an alert or alarm signal. Such an alertor alarm is a component of the GlucoWatch biographer.

A “measurement cycle” typically comprises extraction of an analyte froma subject, using, for example, a sampling device, and sensing of theextracted analyte, for example, using a sensing device, to provide ameasured signal, for example, a measured signal response curve. Acomplete measurement cycle may comprise one or more sets of extractionand sensing.

The term “frequent measurement” refers to a series of two or moremeasurements obtained from a particular biological system, whichmeasurements are obtained using a single device maintained in operativecontact with the biological system over a time period in which a seriesof measurements (e.g, second, minute or hour intervals) is obtained. Theterm thus includes continual and continuous measurements.

The term “subject” encompasses any warm-blooded animal, particularlyincluding a member of the class Mammalia such as, without limitation,humans and nonhuman primates such as chimpanzees and other apes andmonkey species; farm animals such as cattle, sheep, pigs, goats andhorses; domestic mammals such as dogs and cats; laboratory animalsincluding rodents such as mice, rats and guinea pigs, and the like. Theterm does not denote a particular age or sex and, thus, includes adultand newborn subjects, whether male or female.

The term “transdermal” includes both transdermal and transmucosaltechniques, i.e., extraction of a target analyte across skin, e.g.,stratum corneum, or mucosal tissue. Aspects of the invention which aredescribed herein in the context of“transdermal,” unless otherwisespecified, are meant to apply to both transdermal and transmucosaltechniques.

The term “transdermal extraction,” or “transdermally extracted” refersto any sampling method, which entails extracting and/or transporting ananalyte from beneath a tissue surface across skin or mucosal-tissue. Theterm thus includes extraction of an analyte using, for example,iontophoresis (reverse iontophoresis), electroosmosis, sonophoresis,microdialysis, suction, and passive diffusion. These methods can, ofcourse, be coupled with application of skin penetration enhancers orskin permeability enhancing technique such as various substances orphysical methods such as tape stripping or pricking with micro-needles.The term “transdermally extracted” also encompasses extractiontechniques which employ thermal poration, laser microporation,electroporation, microfine lances, microfine cannulas, subcutaneousimplants or insertions, combinations thereof, and the like.

The term “iontophoresis” refers to a method for transporting substancesacross tissue by way of an application of electrical energy to thetissue. In conventional iontophoresis, a reservoir is provided at thetissue surface to serve as a container of (or to provide containmentfor) material to be transported. Iontophoresis can be carried out usingstandard methods known to those of skill in the art, for example byestablishing an electrical potential using a direct current (DC) betweenfixed anode and cathode “iontophoretic electrodes,” alternating a directcurrent between anode and cathode iontophoretic electrodes, or using amore complex waveform such as applying a current with alternatingpolarity (AP) between iontophoretic electrodes (so that each electrodeis alternately an anode or a cathode). For example, see U.S. Pat. Nos.5,771,890 and 6,023,629 and PCT Publication No. WO 96/00109, published 4Jan. 1996.

The term “reverse iontophoresis” refers to the movement of a substancefrom a biological fluid across a membrane by way of an applied electricpotential or current. In reverse iontophoresis, a reservoir is providedat the tissue surface to receive the extracted material, as used in theGlucoWatch® (Cygnus, Inc., Redwood City, Calif.) biographer glucosemonitor (See, e.g., Tamada et al. (1999) JAMA 282:1839-1844).

“Electroosmosis” refers to the movement of a substance through amembrane by way of an electric field-induced convective flow. The termsiontophoresis, reverse iontophoresis, and electroosmosis, will be usedinterchangeably herein to refer to movement of any ionically charged oruncharged substance across a membrane (e.g., an epithelial membrane)upon application of an electric potential to the membrane through anionically conductive medium.

The term “sensing device,” or “sensing mechanism,” encompasses anydevice that can be used to measure the concentration or amount of ananalyte, or derivative thereof, of interest. Preferred sensing devicesfor detecting blood analytes generally include electrochemical devices,optical and chemical devices and combinations thereof. Examples ofelectrochemical devices include the Clark electrode system (see, e.g.,Updike, et al., (1967) Nature 214:986-988), and other amperometric,coulometric, or potentiometric electrochemical devices, as well as,optical methods, for example UV detection or infrared detection (e.g.,U. S. Pat. No. 5,747,806).

A “biosensor” or “biosensor device” includes, but is not limited to, a“sensor element” that includes, but is not limited to, a “biosensorelectrode” or “sensing electrode” or “working electrode” which refers tothe electrode that is monitored to determine the amount of electricalsignal at a point in time or over a given time period, which signal isthen correlated with the concentration of a chemical compound. Thesensing electrode comprises a reactive surface which converts theanalyte, or a derivative thereof, to electrical signal. The reactivesurface can be comprised of any electrically conductive material suchas, but not limited to, platinum-group metals (including, platinum,palladium, rhodium, ruthenium, osmium, and iridium), nickel, copper, andsilver, as well as, oxides, and dioxides, thereof, and combinations oralloys of the foregoing, which may include carbon as well. Somecatalytic materials, membranes, and fabrication technologies suitablefor the construction of amperometric biosensors are described by Newman,J. D., et al.(1995) Analytical Chemistry 67:4594-4599.

The “sensor element” can include components in addition to the sensingelectrode, for example, it can include a “reference electrode” and a“counter electrode.” The term “reference electrode” is used to mean anelectrode that provides a reference potential, e.g., a potential can beestablished between a reference electrode and a working electrode. Theterm “counter electrode” is used to mean an electrode in anelectrochemical circuit that acts as a current source or sink tocomplete the electrochemical circuit. Although it is not essential thata counter electrode be employed where a reference electrode is includedin the circuit and the electrode is capable of performing the functionof a counter electrode, it is preferred to have separate counter andreference electrodes because the reference potential provided by thereference electrode is most stable when it is at equilibrium. If thereference electrode is required to act further as a counter electrode,the current flowing through the reference electrode may disturb thisequilibrium. Consequently, separate electrodes functioning as counterand reference electrodes are preferred.

In one embodiment, the “counter electrode” of the “sensor element”comprises a “bimodal electrode.” The term “bimodal electrode” typicallyrefers to an electrode which is capable of functioningnon-simultaneously as, for example, both the counter electrode (of the“sensor element”) and the iontophoretic electrode (of the “samplingmechanism”) as described, for example, U.S. Pat. No. 5,954,685.

The terms “reactive surface,” and “reactive face” are usedinterchangeably herein to mean the surface of the sensing electrodethat: (1) is in contact with the surface of an ionically conductivematerial which contains an analyte or through which an analyte, or aderivative thereof, flows from a source thereof; (2) is comprised of acatalytic material (e.g., a platinum group metal, platinum, palladium,rhodium, ruthenium, or nickel and/or oxides, dioxides and combinationsor alloys thereof) or a material that provides sites for electrochemicalreaction; (3) converts a chemical signal (for example, hydrogenperoxide) into an electrical signal (e.g., an electrical current); and(4) defines the electrode surface area that, when composed of a reactivematerial, is sufficient to drive the electrochemical reaction at a ratesufficient to generate a detectable, reproducibly measurable, electricalsignal that is correlatable with the amount of analyte present in theelectrolyte.

An “ionically conductive material” refers to any material that providesionic conductivity, and through which electrochemically active speciescan diffuse. The ionically conductive material can be, for example, asolid, liquid, or semi-solid (e.g., in the form of a gel) material thatcontains an electrolyte, which can be composed primarily of water andions (e.g., sodium chloride), and generally comprises 50% or more waterby weight. The material can be in the form of a hydrogel, a sponge orpad (e.g., soaked with an electrolytic solution), or any other materialthat can contain an electrolyte and allow passage of electrochemicallyactive species, especially the analyte of interest. Some exemplaryhydrogel formulations are described in WO 97/02811, published Jan. 30,1997. The ionically conductive material may comprise a biocide. Forexample, during manufacture of an AutoSensor assembly, one or morebiocides may be incorporated into the ionically conductive material.Biocides of interest include, but are not limited to, compounds such aschlorinated hydrocarbons; organometallics; hydrogen releasing compounds;metallic salts; organic sulfur compounds; phenolic compounds (including,but not limited to, a variety of Nipa Hardwicke Inc. liquidpreservatives registered under the trade names Nipastat®, Nipaguard®,Phenosept®, Phenonip®, Phenoxetol®, and Nipacide®); quaternary ammoniumcompounds; surfactants and other membrane-disrupting agents (including,but not limited to, undecylenic acid and its salts), combinationsthereof, and the like.

The term “buffer” refers to one or more components which are added to acomposition in order to adjust or maintain the pH of the composition.

The term “electrolyte” refers to a component of the ionically conductivemedium which allows an ionic current to flow within the medium. Thiscomponent of the ionically conductive medium can be one or more salts orbuffer components, but is not limited to these materials.

The term “collection reservoir” is used to describe any suitablecontainment method or device for containing a sample extracted from abiological system. For example, the collection reservoir can be areceptacle containing a material which is ionically conductive (e.g.,water with ions therein), or alternatively it can be a material, such asa sponge-like material or hydrophilic polymer, used to keep the water inplace. Such collection reservoirs can be in the form of a hydrogel (forexample, in the shape of a disk or pad). Hydrogels are typicallyreferred to as “collection inserts.” Other suitable collectionreservoirs include, but are not limited to, tubes, vials, strips,capillary collection devices, cannulas, and miniaturized etched, ablatedor molded flow paths.

A “collection insert layer” is a layer of an assembly or laminatecomprising a collection reservoir (or collection insert) located, forexample, between a mask layer and a retaining layer.

A “laminate” refers to structures comprised of, at least, two bondedlayers. The layers may be bonded by welding or through the use ofadhesives. Examples of welding include, but are not limited to, thefollowing: ultrasonic welding, heat bonding, and inductively coupledlocalized heating followed by localized flow. Examples of commonadhesives include, but are not limited to, chemical compounds such as,cyanoacrylate adhesives, and epoxies, as well as adhesives having suchphysical attributes as, but not limited to, the following: pressuresensitive adhesives, thermoset adhesives, contact adhesives, and heatsensitive adhesives.

A “collection assembly” refers to structures comprised of severallayers, where the assembly includes at least one collection insertlayer, for example a hydrogel. An example of a collection assembly asreferred to in the present invention is a mask layer, collection insertlayer, and a retaining layer where the layers are held in appropriatefunctional relationship to each other but are not necessarily a laminate(i.e., the layers may not be bonded together. The layers may, forexample, be held together by interlocking geometry or friction).

The term “mask layer” refers to a component of a collection assemblythat is substantially planar and typically contacts both the biologicalsystem and the collection insert layer. See, for example, U.S. Pat. Nos.5,735,273, 5,827,183, and 6,201,979, all herein incorporated byreference.

The term “gel retaining layer” or “gel retainer” refers to a componentof a collection assembly that is substantially planar and typicallycontacts both the collection insert layer and the electrode assembly.

The term “support tray” typically refers to a rigid, substantiallyplanar platform and is used to support and/or align the electrodeassembly and the collection assembly. The support tray provides one wayof placing the electrode assembly and the collection assembly into thesampling system.

An “AutoSensor assembly” refers to a structure generally comprising amask layer, collection insert layer, a gel retaining layer, an electrodeassembly, and a support tray. The AutoSensor assembly may also includeliners where the layers are held in approximate, functional relationshipto each other. Exemplary collection assemblies and AutoSensor structuresare described, for example, in International Publication WO 99/58190,published 18 Nov. 1999; and U.S. Pat. Nos. 5,735,273 and 5,827,183. Themask and retaining layers are preferably composed of materials that aresubstantially impermeable to the analyte (chemical signal) to bedetected; however, the material can be permeable to other substances. By“substantially impermeable” is meant that the material reduces oreliminates chemical signal transport (e.g., by diffusion). The materialcan allow for a low level of chemical signal transport, with the provisothat chemical signal passing through the material does not causesignificant edge effects at the sensing electrode.

The terms “about” or “approximately” when associated with a numericvalue refers to that numeric value plus or minus 10 units of measure(i.e. percent, grams, degrees or volts), preferably plus or minus 5units of measure, more preferably plus or minus 2 units of measure, mostpreferably plus or minus 1 unit of measure.

By the term “printed” is meant a substantially uniform deposition of anelectrode formulation onto one surface of a substrate (i.e., the basesupport). It will be appreciated by those skilled in the art that avariety of techniques may be used to effect substantially uniformdeposition of a material onto a substrate, e.g., Gravure-type printing,extrusion coating, screen coating, spraying, painting, electroplating,laminating, or the like.

The term “physiological effect” encompasses both positive and negativeeffects on the physiology of a subject. One example of a positivephysiological effect, is a treatment of a subject that achieves thepurpose of the therapy. Typically, a positive physiological effect meansthat the symptoms of the subject being treated are prevented oralleviated. For example, a positive physiological effect would be theprolongation of survival in a patient being treated for one or morecondition or disorder An example of negative physiological effects isthe effect of severe hypoglycemia in a human subject, such negativephysiological effects may include confusion, blurring of vision,seizure, and ultimately loss of consciousness or seizure.

“Parameter” refers to an arbitrary constant or variable so appearing ina mathematical expression that changing it gives various cases of thephenomenon represented (McGraw-Hill Dictionary of Scientific andTechnical Terms, S. P. Parker, ed., Fifth Edition, McGraw-Hill Inc.,1994). A parameter is any of a set of properties whose values determinethe characteristics or behavior of something.

“Decay” refers to a gradual reduction in the magnitude of a quantity,for example, a current detected using a sensor electrode where thecurrent is correlated to the concentration of a particular analyte andwhere the detected current gradually reduces but the concentration ofthe analyte does not.

“Skip” or “skipped” signals refer to data that do not conform topredetermined criteria (for example, error-associated criteria asdescribed in U.S. Pat. No. 6,233,471, herein incorporated by reference).A skipped reading, signal, or measurement value typically has beenrejected (i.e., a “skip error” generated) as not being reliable or validbecause it does not conform with data integrity checks, for example,where a signal is subjected to a data screen which invalidates incorrectsignals based on a detected parameter indicative of a poor or incorrectsignal.

A “data point”, generally, is a numeric value which corresponds to aphysical measurement (an “acquired” datum or data point) or to a singlenumeric result calculated or derived from one or more acquired datapoints (a “calculated” or “derived” datum or data point). Derived datainclude, but are not limited to, derived quantities from original data,such as, rate and/or magnitude of change, slope of a line (e.g., asdetermined by regression analysis), an intercept (e.g., as determined byregression analysis), and correlation coefficients.

“Data tags,” also referred to as “attributes” of a data point, arevarious characteristics of the particular data point with which they areassociated. For example, data points comprising glucose concentrationsor amounts measured with the GlucoWatch biographer are associated with anumber of attributes, e.g., the date and time the measurement was taken;certain identification related to the particular user from which themeasurement was made (e.g., demographic information such as theparticular user's sex, age, weight; medical information e.g., the typeof disease suffered by the user).

A “database” is a collection of data points and data attributesassociated with each data point. Thus, an “analyte data points, deriveddata, and data attributes database” is a database comprising data pointscollected, e.g. by an analyte monitoring device, data derived from theoriginal data points and the data attributes associated with those datapoints or the derived data. A database may be limited to data pointscomprising measurements of one or more analyte levels; those data pointsmay further be collected from one or more subjects. For example, oneanalyte data point database may be created and the information in thedatabase related to a second database of attributes. Such combinationsof one or more databases are within the skill of one of ordinary skillin the art in view of the teachings of the present specification. A“data warehouse” is another term for database. The term data warehouseis typically applied to large databases.

“Formulation” of a database comprises collecting data points, inputingthose data points into a desired database format, and associatingvarious attributes with each data point according to the particularformat employed. A wide variety of software exists which provides ameans for inputing data points, and associating the data points withdata attributes, such as Excel® (Microsoft® Corporation, Seattle, Wash.)spreadsheet software, Quattro® (Corel Inc., Ottawa, Canada) spreadsheetsoftware, Microsoft Access 2000® (Microsoft) software, Oracle® (OracleInc., Redwood Shores, Calif.) software, as well as other database anddata warehousing software.

“Manipulation” of a database refers to a variety of processes, e.g.,selecting, sorting, sifting, aggregating, clustering, modeling,exploring, and segmenting data points using various data attributes ortags associated with the data points. Available systems for generatingdatabases and manipulating the resulting databases include but are notlimited to Sybase® (Sybase Systems, Emeryville, Calif.), Oracle® (OracleInc., Redwood Shores, Calif.), and Sagent Design Studio® (SagentTechnologies Inc., Mountain View, Calif.) systems software. Further,statistical packages and systems for data analysis and data mining arealso available. Illustrative examples include SAS® (SAS Institute Inc.,Cary, N.C.) and SPSS® (SPSS Inc., Chicago, Ill.) systems software.

“Data mining” refers to the process of selecting, exploiting, modeling,etc., large amounts of data to uncover previously unknown trends,patterns, and relationships within and among various data points anddata attributes.

“Data aggregation” and “data clustering” refers to the process ofgrouping data points on the basis of one or more common attributes.Conversely, “data segmentation” refers to the process of differentiatingdata into discrete groups on the basis of one or more attributes.

2. Modes of Carrying Out the Invention

Before describing the present invention in detail, it is to beunderstood that this invention is not limited to particular formulationsor process parameters as such may, of course, vary. It is also to beunderstood that the terminology used herein is for the purpose ofdescribing particular embodiments of the invention only, and is notintended to be limiting.

Although a number of methods and materials similar or equivalent tothose described herein can be used in the practice of the presentinvention, the preferred materials and methods are described herein.

2.1 General Overview of the Invention

The present invention relates to a method for formulating an analytedata points, derived data, and data attributes database (referred toherein as analyte data databases), comprising data points correspondingto measured values of one or more analytes collected by an analytemeasurement device, and one or more relevant attributes associated witheach data point. The present invention also relates to analyte datadatabases.

Further, the invention relates to the manipulation of a database of thepresent invention by means of data attributes in order to examinefactors which affect baseline analyte levels and/or changes thereto: forexample, analysis of data related to physiological effects, such as therate of change of an analyte in a subject. The analyte data databasesmay also be manipulated through the data points, derived data, and/ordata attributes, as well as combinations thereof.

Recent advances in analyte monitoring device technology now permitperiodic measurement of analyte levels for a large number of users witha consistency and frequency that was not previously possible. Forexample, the GlucoWatch biographer automatically measures a user's bloodglucose levels approximately three times every hour. Thus, analytemonitoring devices such as the GlucoWatch biographer make it possible togenerate sufficient data regarding analyte levels to permit theformulation of analyte data points, derived data, and data attributesdatabases of unprecedented size and scope.

Tagging the data points after collection permits manipulation of thedatabase to produce useful information, e.g., information regardingfactors that affect or are correlated with baseline analyte levelsand/or changes therein.

2.2 Database Formulation

The method of formulating an analyte data points, derived data, and dataattributes database (i.e., analyte data databases) according to thepresent invention may comprise the following: (1) the periodiccollection of data points, said data points comprising measurements madeby an analyte monitoring device, for example, a current measurement thatis ultimately correlated with an analyte amount or concentration; and(2) the association of those data points with relevant data pointattributes. The method may further comprise (3) determining derived datapoints from one or more direct data points and (4) associating thosedata points with relevant data point attributes.

In one version of the present invention, the data points are collectedfrom a single individual. In an alternate embodiment, data points arecollected from multiple individuals and compiled into an aggregate orpopulation database, either at the time the points are collected orsubsequently.

The analyte monitoring device used to collect data points may be capableof measuring a single analyte. Alternately, the device may be capable ofmeasuring multiple analytes, and data points for each are collected andcompiled into a multi-analyte data database. In still anotherembodiment, the database is formulated by compiling data pointscollected using several analyte monitors, each of which measures asingle analyte, resulting in a multi-analyte data database. Examples ofanalytes, both biological and non-biological, are detailed in thesubsection herein entitled “Exemplary Analytes.”

In the case of multi-analyte data databases, wherein the analytes arebiological analytes, all of the analytes may be related to a singlephysiologic state or condition; alternately, each analyte may berelevant to a different physiological state or condition. Details ofexemplary data points, exemplary data attributes, and exemplary methodsfor associating data points with relevant data attributes are describedin detail in the following subsections.

An exemplary analyte monitoring device is a glucose monitoring systemwhich provides frequent measurements of glucose amount orconcentrations, e.g., the GlucoWatch biographer system. This system is awearable, non-invasive glucose monitoring system that provides a glucosereading automatically every twenty minutes. The GlucoWatch biographersystem has several advantages including, but not limited to, the factthat its non-invasive and non-obtrusive nature encourages more frequentglucose testing among people (or animals) with diabetes. Of greaterclinical relevance is the frequent nature of the information provided.Prior to the GlucoWatch biographer system no method existed for frequentglucose measurement outside of invasive means, often requiring hospitalcare (Mastrototaro, J. J., and Gross, T. M., “Clinical Results from theMiniMed Continuous Glucose Monitoring System” Proc. 31^(st) Annual OakRidge Conference, April, 1999). The GlucoWatch biographer systemprovides more frequent monitoring often desired by physicians in anautomatic, non-invasive, and user-friendly manner. The automatic natureof the system also allows monitoring to continue even while a user issleeping or otherwise unable to test.

The GlucoWatch biographer system comprises: (a) iontophoretic transportof glucose across the skin to non-invasively sample the glucose in thesubject, (b) an electrochemical biosensor to measure the glucoseconcentration in the extracted sample, and (c) an intelligentdata-processing algorithm that coverts the raw biosensor signals toglucose readings while safeguarding against erroneous results throughdata point screening routines. These three aspects of the system arebriefly described below and are described more extensively in thepublications referenced in the “Definitions” section, above.

The first aspect of the system is the iontophoretic extraction ofglucose. Many small molecules are transported through the skin by eitherpassive or facilitated means. Passive transport of compounds such asnicotine, estradiol, testosterone, etc. is the basis of transdermal drugdelivery (skin patches). Transport through human skin can be greatlyenhanced by the application of an electric field gradient. The use of alow-level electric current to enhance transport is known, generically,as iontophoresis. Iontophoretic transport through skin can occur ineither direction (Glikfeld, P., et al., Pharm. Res. 6, 988-990 (1989)).In particular, it was shown that small molecules such as glucose,ethanol, and theophylline are readily transported through the skin intoan external collection chamber. Because transport through the skin is inthe opposite direction to that used in iontophoretic drug delivery, thiseffect was described as “reverse iontophoresis” (U.S. Pat. No.5,362,307, issued Nov. 8, 1994.; U.S. Pat. No. 5,279,543, issued Jan.18, 1994.; U.S. Pat. No. 5,730,714, issued Mar. 24, 1998). In fact,because glucose is an uncharged molecule, transport is achieved throughelectro-osmosis. Results obtained from analyses using the GlucoWatchbiographer system showed that extracted glucose correlated closely withblood glucose (Tamada, J. A., et al., JAMA 282:1839-1844, 1999).

The second aspect of the system involves the use of an electrochemicalglucose biosensor. The GlucoWatch biographer system utilizes anelectrochemical biosensor assembly to quantitate the glucose extractedthrough the skin. There are two biosensors in the GlucoWatch biographersystem (FIG. 9). Each biosensor consists of a hydrogel pad containingthe enzyme glucose oxidase (GOx) and a set of electrodes. One surface ofthe hydrogel pad contacts the skin while the opposite surface is incontact with the biosensor and iontophoresis electrodes. The hydrogelpads serve two functions as follows. During iontophoresis the pads serveas the electrical contacts with the skin and the collection reservoirsfor the extracted glucose. During the sensing portion of the cycle, theglucose extracted through the skin reacts with the GOx in the hydrogelpads via the reaction:

The H₂O₂ produced by this reaction is then detected amperometrically atthe platinum/carbon working electrode of the sensor. The integratedsensor current measured is proportional to the concentration of H₂O₂,and ultimately to the amount of glucose extracted. The extraction andsensing portions of the cycle occur in succession, and the cycle repeatsto provide a measurement of glucose every twenty minutes.

For convenience to the user, the GlucoWatch biographer system wasdeveloped as a miniaturized device which can be worn on the wrist,forearm, upper arm, or other body part. The GlucoWatch biographer systemdurable component contains electronics for the biosensors andiontophoresis, a microprocessor, data storage memory, and an LCDdisplay. Two sets of biosensors and iontophoresis electrodes are fittedonto the skin side of the device (e.g., a consumable component, theAutoSensor). A schematic diagram of the AutoSensor of the GlucoWatchbiographer system is shown in FIG. 9.

Referring to FIG. 9, an exploded view of exemplary components comprisingone embodiment of an AutoSensor for use in an iontophoretic samplingsystem is presented. The AutoSensor components include twobiosensor/iontophoretic electrode assemblies, 904 and 906, each of whichhave an annular iontophoretic electrode, respectively indicated at 908and 910, which encircles a biosensor electrode 912 and 914. Theelectrode assemblies 904 and 906 are printed onto a polymeric substrate916 which is maintained within a sensor tray 918. A collection reservoirassembly 920 is arranged over the electrode assemblies, wherein thecollection reservoir assembly comprises two hydrogel inserts 922 and 924retained by a gel retaining layer 926 and mask layer 928. Furtherrelease liners may be included in the assembly, for example, a patientliner 930, and a plow-fold liner 932. In one embodiment, the electrodeassemblies comprise bimodal electrodes. A mask layer 928 (for example,as described in PCT Publication No. WO 97/10356, published 20 Mar. 1997,and U.S. Pat. Nos. 5,735,273, 5,827,183, 6,141,573, and 6,201,979, allherein incorporated by reference) may be present. Other AutoSensorembodiments are described in WO 99/58190, published 18 Nov. 1999, hereinincorporated by reference.

The mask and retaining layers are preferably composed of materials thatare substantially impermeable to the analyte (e.g., glucose) to bedetected (see, for example, U.S. Pat. Nos. 5,735,273, and 5,827,183,both herein incorporated by reference). By “substantially impermeable”is meant that the material reduces or eliminates analyte transport(e.g., by diffusion). The material can allow for a low level of analytetransport, with the proviso that the analyte that passes through thematerial does not cause significant edge effects at the sensingelectrode used in conjunction with the mask and retaining layers.Examples of materials that can be used to form the layers include, butare not limited to, polyester, polyester derivatives, otherpolyester-like materials, polyurethane, polyurethane derivatives andother polyurethane-like materials.

The components shown in exploded view in FIG. 9 are for use in aautomatic sampling system which is configured to be worn like anordinary wristwatch, as described, for example, in PCT Publication-No.WO 96/00110, published 4 Jan. 1996, herein incorporated by reference.The wristwatch housing can further include suitable electronics (e.g.,one or more microprocessor(s), memory, display and other circuitcomponents) and power sources for operating the automatic samplingsystem. The one or more microprocessors may control a variety offunctions, including, but not limited to, control of a sampling device,a sensing device, aspects of the measurement cycle (for example, timingof sampling and sensing, and alternating polarity between electrodes),connectivity, computational methods, different aspects of datamanipulation (for example, acquisition, recording, recalling, comparing,and reporting), etc.

The third aspect of the system is an intelligent data-processingalgorithm that coverts the raw biosensor signals to glucose readingswhile safeguarding against erroneous results through data pointscreening routines. The raw current data obtained from the biosensorsmust be converted into an equivalent blood glucose value. Equations toperform this data conversion have been developed, optimized, andvalidated on a large data set consisting of GlucoWatch and referenceblood glucose readings from clinical trials on diabetic subjects (see,for example, WO 018289A1, published 6 Apr. 2000). This data conversionalgorithm is programmed into a dedicated microprocessor in theGlucoWatch biographer system. The software also contains screens toexclude spurious data points that do not conform to objective, a prioricriteria (e.g., data which contain noise above a certain threshold).Exemplary signal processing applications include, but are not limitedto, those taught in U.S. Pat. Nos. 6,144,869, 6,233,471, 6,180,416,herein incorporated by reference.

In addition to the two glucose biosensors, the GlucoWatch biographersystem also contains a temperature sensor and a skin conductivitysensor. Input from the former is used to exclude data points obtainedduring large thermal excursions. The skin conductivity input is used toexclude data obtained when the subject is perspiring profusely, as sweatcontains glucose which may confound the value obtained for the extractedsample. Hence, these various screens reject data points that may providefalse glucose information. The remaining data points are then suitablefor clinical use.

The GlucoWatch biographer system is housed in a plastic case held inplace, typically on the arm, with a wrist band. A single AAA battery isused as the primary power source with an additional back-up battery. TheGlucoWatch circuitry includes a microprocessor and a custom applicationspecific integrated circuit (ASIC) chip containing the circuitry to runboth the iontophoresis and biosensor functions. There is sufficientmemory to store up to 4000 glucose readings which representsapproximately three months of data with daily use. An LCD display andfour push buttons on the face of the GlucoWatch biographer systemcomprise the user interface, and allow the user to control and customizethe functions of the monitor as well as to display clock time and date,glucose readings, and GlucoWatch operation status. Data can also bedownloaded to, for example, a PC via a serial interface-adapter.

Included in the software control is the ability for the user to selecthigh and low glucose alert levels. If the GlucoWatch biographer systemmeasures a glucose value outside of these alert levels, an alarm soundsto notify the user of the situation.

The disposable portion of the GlucoWatch biographer system is theAutoSensor, which contains the two sets of biosensor and iontophoresiselectrodes and the corresponding hydrogel discs housed held in apre-aligned arrangement by a mask layer. The AutoSensor snaps into theskin-side of the GlucoWatch biographer system to make the necessaryelectrical connections between the two portions.

The GlucoWatch biographer system also contains a thermistor to measureskin temperature, and a set of conductivity probes which rest on thesurface of the skin to measure skin conductivity, a measure ofperspiration. As described above, the temperature and sweat data areused in the present device to ensure that the biosensor data has notbeen affected by large temperature excursions or perspiration during thereading period.

In another embodiment of a monitoring system, the sampling/sensingmechanism and user interface may be found on separate components (e.g.,WO 00/47109, published 17 Aug. 2000). Thus, the monitoring system cancomprise at least two components, in which a first component comprisessampling mechanism and sensing mechanism that are used to extract anddetect an analyte, for example, glucose, and a second component thatreceives the analyte data from the first component, conducts dataprocessing on the analyte data to determine an analyte concentration andthen displays the analyte concentration data. Typically, microprocessorfunctions (e.g., control of a sampling device, a sensing device, aspectsof the measurement cycle, computational methods, different aspects ofdata manipulation or recording, etc.) are found in both components.Alternatively, microprocessing components may be located in one or theother of the at least two components. The second component of themonitoring system can assume many forms, including, but not limited to,the following: a watch, a credit card-shaped device (e.g., a “smartcard” or “universal card” having a built-in microprocessor as describedfor example in U.S. Pat. No. 5,892,661, herein incorporated byreference), a pager-like device, cell phone-like device, or other suchdevice that communicates information to the user visually, audibly, orkinesthetically.

Further, additional components may be added to the system, for example,a third component comprising a display of analyte values or an alarmrelated to analyte concentration, may be employed. In certainembodiments, a delivery unit is included in the system. An exemplarydelivery unit is an insulin delivery unit. Insulin delivery units, bothimplantable and external, are known in the art and described, forexample, in U.S. Pat. Nos. 5,995,860; 5,112,614 and 5,062,841, hereinincorporated by reference. Preferably, when included as a component ofthe present invention, the delivery unit is in communication (e.g.,wire-like or wireless communication) with the extracting and/or sensingmechanism such that the sensing mechanism can control the insulin pumpand regulate delivery of a suitable amount of insulin to the subject.

Advantages of separating the first component (e.g., including thebiosensor and iontophoresis functions) from the second component (e.g.,including some microprocessor and display functions) include greaterflexibility, discretion, privacy and convenience to the user. Having asmall and lightweight measurement unit allows placement of the twocomponents of the system on a wider range of body sites, for example,the first component may be placed on the abdomen or upper arm. Thiswider range of placement options may improve the accuracy throughoptimal extraction site selection (e.g., torso rather than extremities)and greater temperature stability (e.g., via the insulating effects ofclothing). Thus, the collection and sensing assembly will be able to beplaced on a greater range of body sites. Similarly, a smaller and lessobtrusive microprocessor and display unit (the second component)provides a convenient and discrete system by which to monitor analytes.The biosensor readouts and control signals will be relayed via wire-likeor wireless technology between the collection and sensing assembly andthe display unit which could take the form of a small watch, a pager, ora credit card-sized device. This system also provides the ability torelay an alert message or signal during nighttime use, for example, to asite remote from the subject being monitored.

In one embodiment, the two components of the device can be in operativecommunication via a wire or cable-like connection. Operativecommunications between the components can be wireless link, i.e.provided by a “virtual cable,” for example, a telemetry link. Thiswireless link can be uni- or bi-directional between the two components.In the case of more than two components, links can be a combination ofwire-like and wireless.

2.3 Exemplary Analytes

The analyte can be any specific substance, component, or combinationsthereof that one is desirous of detecting and/or measuring in achemical, physical, enzymatic, or optical analysis. Such analytesinclude, but are not limited to, amino acids, enzyme substrates orproducts indicating a disease state or condition, other markers ofdisease states or conditions, drugs of abuse (e.g., ethanol, cocaine),therapeutic and/or pharmacological agents (e.g., theophylline, anti-HIVdrugs, lithium, anti-epileptic drugs, cyclosporin, chemotherapeutics),electrolytes, physiological analytes of interest (e.g., urate/uric acid,carbonate, calcium, potassium, sodium, chloride, bicarbonate (CO₂),glucose, urea (blood urea nitrogen), lactate and/or lactic acid,hydroxybutyrate, cholesterol, triglycerides, creatine, creatinine,insulin, hematocrit, and hemoglobin), blood gases (carbon dioxide,oxygen, pH), lipids, heavy metals (e.g., lead, copper), environmentalanalytes (e.g., pesticides) and the like. Analytes in non-biologicalsystems may also be evaluated using the methods of the presentinvention.

In preferred embodiments, the analyte is a physiological analyte ofinterest, for example glucose, or a chemical that has a physiologicalaction, for example a drug or pharmacological agent. Further, multipleanalytes may be evaluated using a single sampling mechanism and thesame, or different, sensing means.

In order to facilitate detection of the analyte, an enzyme (or enzymes)can be disposed within the one or more collection reservoirs. Theselected enzyme is capable of catalyzing a reaction with the extractedanalyte to the extent that a product of this reaction can be sensed,e.g., can be detected electrochemically from the generation of a currentwhich current is detectable and proportional to the amount of theanalyte which is reacted. In one embodiment of the present invention, asuitable enzyme is glucose oxidase, which oxidizes glucose to gluconicacid and hydrogen peroxide. The subsequent detection of hydrogenperoxide on an appropriate biosensor electrode generates two electronsper hydrogen peroxide molecule creating a current that can be detectedand related to the amount of glucose entering the device. Glucoseoxidase (GOx) is readily available commercially and has well knowncatalytic characteristics. However, other enzymes can also be used, aslong as they specifically catalyze a reaction with an analyte orsubstance of interest to generate a detectable product in proportion tothe amount of analyte so reacted.

In like manner, a number of other analyte-specific enzyme systems can beused in the invention, which enzyme systems operate on much the samegeneral techniques. For example, a biosensor electrode that detectshydrogen peroxide can be used to detect ethanol using an alcohol oxidaseenzyme system, or similarly uric acid with urate oxidase system, ureawith a urease system, cholesterol with a cholesterol oxidase system, andtheophylline with a xanthine oxidase system.

In addition, the oxidase enzyme (used for hydrogen peroxidase-baseddetection) can be replaced with another redox system, for example, thedehydrogenase-enzyme NAD-NADH, which offers a separate route todetecting additional analytes. Dehydrogenase-based sensors can useworking electrodes made of gold or carbon (via mediated chemistry).Examples of analytes suitable for this type of monitoring include, butare not limited to, cholesterol, ethanol, hydroxybutyrate,phenylalanine, triglycerides, and urea. Further, the enzyme can beeliminated and detection can rely on direct electrochemical orpotentiometric detection of an analyte. Such analytes include, withoutlimitation, heavy metals (e.g., cobalt, iron, lead, nickel, zinc),oxygen, carbonate/carbon dioxide, chloride, fluoride, lithium, pH,potassium, sodium, and urea. Also, the sampling system described hereincan be used for therapeutic drug monitoring, for example, monitoringanti-epileptic drugs (e.g., phenytion), chemotherapy (e.g., adriamycin),hyperactivity (e.g., ritalin), and anti-organ-rejection (e.g.,cyclosporin).

Preferably, the biosensor electrode is able to detect the analyte thathas been extracted into the one or more collection reservoirs whenpresent at nominal concentration levels. Suitable biosensor electrodesand associated sampling systems as described in are described in PCTPublication Nos. WO 97/10499, published 20 Mar. 1997 and WO 98/42252,published 1 Oct. 1998.

2.4 Data Points

The method of formulating an analyte data points, derived data, and dataattributes database that is the subject of the present invention beginswith the collection of data sets of analyte measurement values.Typically, those sets comprise different values collected at frequent,periodic intervals over a relatively long time span. In a preferredembodiment of the invention, those data points are collected using anautomatic analyte monitoring device, e.g. the GlucoWatch biographerdescribed above For example, FIGS. 1 and 2 are tabulations of unanalyzeddata collected using a GlucoWatch biographer from a single individualduring a single data collection run. FIGS. 1A/1B represents data pointscollected prior to calibrating the biographer device, while FIG. 2represents data points collected after calibrating the device.Calibration was carried out using a single blood glucose value obtainedusing a finger stick glucometer, e.g., the HemoCue clinical analyzer.

The data attributes represented by each of the columns in FIGS. 1 and 2are as follows. Column 1 provides a reference record number denominatedMGLOG RECORD #. Column 2 provides information regarding the status ofthe record, i.e., whether the record is OK or CORRUPT, the latterindicating e.g., the occurrence of a memory error or problem caused by awrite-to-memory error or a microprocessor problem. Column 3 providesinformation regarding the LOG ENTRY TYPE, a number that refers to thetype of record found in that row. 1=MION, i.e., an iontophoretic record;2=MBIO, i.e., a biosensor record; 3=START, NEWBAT, etc., i.e., externalevents; and 4=SWEAT, i.e., a sweat record. Column 4 lists the RECORDTYPE i.e., the acronym for the record type corresponding to the numberpresent in the LOG ENTRY TYPE column.

Note that each row contains data for only one record type, the typebeing indicated numerically in the LOG ENTRY TYPE column and via analphabetic acronym in the RECORD TYPE column. Hence, rows that containMION records, i.e., data related to the generation of iontophoreticcurrent used to effect transdermal glucose, will not have entries incolumns 13 through 17. Similarly, rows that contain SWEAT records (i.e.,data related to readings made by the device's sweat probe) will not haveentries in columns 11 through 16. Likewise, rows containing MBIO records(i.e., data related to the detection of the amount or concentration ofglucose extracted transdermally) will not have entries in columns 11, 12or 17.

Column 5 provides the date on which the measurement was performed.Column 6 provides the time of day at which the measurement wasperformed. Column 7 provides a SEQUENCE NUMBER, i.e., the datacollection sequence being run for the collected row. Column 8 is aninformation slot not used in compilation of the figures. Column 9provides TEMPERATURE information in the range of 17-32.4° C. based on atemperature reading taken by a thermocouple internal to the device.Column 10 provides the ELAPSED TIME, a data point relating the elapsedtime since the watch has been placed in contact with a subject. Notethat the ELAPSED TIME column does not contain data points in those rowswhich contain MBIO (biosensor) records, i.e., records related to thefunctioning of the biographer device; instead, in those rows, dataregarding elapsed time is instead recorded in rows 14 and 16. Column 11provides information concerning the iontophoretic voltage used fortransdermal extraction, IONTO VOLTAGE. Column 12 provides informationconcerning the iontophoretic current used for transdermal extraction,IONTO CURRENT. Column 13 provides data regarding the current generatedat a first sensor, wherein the current is generated based on the amountor concentration of glucose as detected using an electrochemical system,see, e.g., as described in U.S. Pat. No. 5,989,409, issued 23 Nov. 1999,SENSOR A CURRENT. Column 14 provides data regarding the amount of timethat the biosensor of sensor A was used to evaluate the glucose-relatedcurrent, SENSOR A ELAPSED BIOSENSOR TIME. Column 15 provides dataregarding the current generated at a second sensor, wherein the currentis generated based on the amount or concentration of glucose as detectedusing an electrochemical system, see, e.g., as described in U.S. Pat.No. 5,989,409, issued 23 Nov. 1999, SENSOR B CURRENT. Column 16 providesdata regarding the amount of time that the biosensor of sensor B wasused to evaluate the glucose-related current, SENSOR B ELAPSED BIOSENSORTIME. Finally, column 17 provides data regarding a SWEAT READING using asweat probe.

Both FIGS. 1 and 2 present extensive data sets comprising currentmeasurements made at independent sensors which are ultimately used todetermine blood glucose concentration along with other selectedparameters (e.g., using, for example, a Mixtures of Experts algorithm asdescribed in WO018289A1, published Apr. 6, 2000).

For example, FIG. 3 depicts a database comprising blood glucose levelvalues (“BG Reading”) derived from acquired data collected from a singleindividual using the GlucoWatch biographer, as well as a variety ofattributes (or records) associated with each of those data points. Thedata appearing in highlighted row “4” (FIG. 3) was derived from the datapresented in FIG. 2.

In FIG. 3, the attributes associated with each column are as follows.Column 1 provides a reference record number, denominated the MGLOGRECORD #. Column 2 provides information regarding the status of therecord, i.e., whether the record is OK or CORRUPT, indicating theoccurrence of a memory error or problem caused by e.g., awrite-to-memory error or a microprocessor problem. Column 3 provides thedate that the measurement was performed, and column 4 provides the timeof day at which the measurement was performed.

Column 5 provides associated event codes (which in the case of thebiographer are predefined in the case of user inputs), as follows. (1)UNCAL indicates that the biographer device is uncalibrated, and isprimarily used to indicate in the MGLOG the start of a monitoring run.(2) START indicates the start time and date in the MLOG and MGLOG. (3)CAL indicates that the user performed a device calibration. (4) ERRindicates that a skip or a shutoff error in a given cycle of the MGLOGhas occurred. (5) MEAL indicates that the user has consumed a meal;likewise, (6) SNACK indicates that the user has consumed a snack. (7)SLEEP indicates that the user is going to sleep. (8) INSULIN indicates apoint at which the user administered insulin. (9) GL OK, indicates thatglucose levels are within a predetermined range; (10) LOLIM, indicatesthat glucose levels have fallen below a predetermined minimum, i.e., ahypoglycemic event. Finally, (11) ENDSEQ indicates the final reading ina sequence of readings.

Column 6 provides a blood glucose reading when such a reading can bereported, i.e., no error has interfered with calculation. Column 7provides a CHECK SUM, i.e., an ASCII representation of all thecharacters in a record row of the MGLOG; this serves to ensure dataintegrity.

The database formulation method of the present invention may furthercomprise the calculation of derived or calculated data points from oneor more acquired data points. A variety of derived data points may beuseful in providing information about individuals or groups duringsubsequent database manipulation, and are therefore typically includedduring database formulation. Derived data points include, but are notlimited to the following: (1) maximum analyte level or concentration,determined for a selected time period (day/week/etc.); (2) minimumanalyte amount or concentration, determined for a selected time period(day/week/etc.); (3) total excursion of analyte values for a selectedtime period, defined as the difference between the maximum and minimumanalyte values for that time period; (4) mean analyte measurement valuefor a selected time period; (5) distribution of analyte values aroundthe mean ( i.e., standard deviation) for a selected time period; (6) thenumber of analyte measurements that are abnormally high or low,determined by comparing a given measurement data point with a selectedvalue; (7) the number of measurements not determined, due to measurementdevice malfunction, over a selected time period; (8) the number of alarmevents for a selected time period; (9) patient-specific alert status;and (10) rates or magnitude of change for any selected data point orderived data point for a selected period of time. Other derived datapoints will be apparent to persons of ordinary skill in the art in lightof the teachings of the present specification. The amount of availabledata and data derived from (or arrived at through analysis of) theoriginal data provide provides an unprecedented amount of informationthat is very relevant to management of diabetic diseases. For example,by examining factors associated with the number of measurements notdetermined, factors can be identified to help reduce this number. Asanother example, examining factors associated with periods of timeidentified with rapid rates of change of glucose values can lead to theidentification of causal factors (e.g., by comparing a record of caloricintake/output times and types with a record of rapid changes in glucosevalues over a selected time period, an association with insufficient orbadly distributed caloric daily carbohydrate intake may be identified asthe cause or periods of strenuous exercise may be identified as thecause, thus allow a subject to modify behavior to better managedisease).

2.5 Data Attributes

Analyte measurements and derived data points are collected andcalculated, respectively, and may be associated with one or more dataattributes to form a database.

Data attributes are typically of several general types. The firstcategory comprises attributes automatically input by the analytemeasurement device. These include but are not limited to the following,several of which may be identified in, for example, FIGS. 2 and 3:chronological information (e.g., DATE and TIME); user perspirationlevels (SWEAT READING); and device operating temperature (TEMPERATURE).Other such attributes may include, but are not limited to, thefollowing: missed measurements; “skipped” measurements; user bodytemperature; user skin conductance; environmental variables (e.g.,temperature, temperature changes, humidity, sun exposure, etc.) andnumber and type (e.g., hyperglycemic or hypoglycemic) of alarm events.

The second category comprises user inputs, including but not limited tothe following: activity codes associated with various activitiesaffecting analyte levels, such as caloric intake and/or output (e.g.,food, physical activity, etc.), sleep and administration of medications,including the dose and time thereof (e.g., FIGS. 1A/1B, EVENT CODE).

The third comprises data identifiers, i.e., attributes defined in termsof analyte values (maximum/minimum; hypoglycemic- orhyperglycemic-events; etc.) and/or may further include informationregarding how these values were obtained, for example, total excursion,mean value, or a statistical distribution or function.

The final category comprises subject identifiers, i.e. characteristicsassociated with a particular subject. These identifiers include but arenot limited to the following: (1) a subject code (e.g., a numeric oralpha-numeric sequence); (2) demographic information such as race,gender and age; (3) physical characteristics such as weight, height andbody mass index (BMI); (4) selected aspects of the subject's medicalhistory (e.g., number of pregnancies, disease states or conditions,etc.); and (5) disease-associated characteristics such as the type ofanalyte disorder, if any; the type of medication used by the subject, ifany; and the presence or absence of surrogate analyte markers (e.g., inthe case of diabetes, HbAIc, a hemoglobin surrogate marker for highblood glucose). For example, the data presented in FIGS. 1, 2 and 3 wasobtained from a single subject. In the practice of the presentinvention, each data point would typically be identified with theparticular subject, as well as the demographic, etc. characteristic ofthat subject.

Other data attributes will be apparent to persons of ordinary skill inthe art in light of the teachings of the present specification.

2.6 Storage of Data Sets and Association of Data Points with RelevantData Attributes

A number of formats exist for storing data sets and simultaneouslyassociating related attributes, including but not limited to (1)tabular, (2) relational, and (3) dimensional. In general the databasescomprise data points, a numeric value which correspond to physicalmeasurement (an “acquired” datum or data point) or to a single numericresult calculated or derived from one or more acquired data points thatare obtained using the various methods disclosed herein. The databasescan include raw data or can also include additional related information,for example data tags also referred to as “attributes” of a data point.The databases can take a number of different forms or be structured in avariety of ways.

The most familiar format is tabular, commonly referred to as aspreadsheet (e.g., FIGS. 1, 2, and 3). A variety of spreadsheet programsare currently in existence, and are typically employed in the practiceof the present invention, including but not limited to Microsoft Excelspreadsheet software and Corel Quattro spreadsheet software. In thisformat, association of data points with related attributes occurs byentering a data point and attributes related to that data point in aunique row at the time the analyte measurement occurs.

Further, rational, relational (Database Design for Mere Mortals, byMichael J. Hernandez, 1997, Addison-Wesley Pub. Co., publisher; DatabaseDesign for Smarties, by Robert J. Muller, 1999, Morgan KaufmannPublishers, publisher; Relational Database Design Clearly Explained, byJan L. Harrington, 1998, Morgan Kaufmann Publishers, publisher) anddimensional (Data-Parallel Computing, by V. B. Muchnick, et al., 1996,International Thomson Publishing, publisher; Understanding FourthDimensions, by David Graves, 1993, Computerized Pricing Systems,publisher) database systems and management may be employed as well.

Relational databases typically support a set of operations defined byrelational algebra. Such databases typically include tables composed ofcolumns and rows for the data included in the database. Each table ofthe database has a primary key, which can be any column or set ofcolumns, the values for which uniquely identify the rows in a table. Thetables in the database can also include a foreign key that is a columnor set of columns, the values of which match the primary key values ofanother table. Typically, relational databases also support a set ofoperations (e.g., select, join and combine) that form the basis of therelational algebra governing relations within the database.

Such relational databases can be implemented in various ways. Forinstance, in Sybase® (Sybase Systems, Emeryville, Calif.) databases, thetables can be physically segregated into different databases. WithOracle® (Oracle Inc., Redwood Shores, Calif.) databases, in contrast,the various tables are not physically separated, because there is oneinstance of work space with different ownership specified for differenttables. In some configurations, databases are all located in a singledatabase (e.g., a data warehouse) on a single computer. In otherinstances, various databases are split between different computers.

It should be understood, of course, that the databases are not limitedto the foregoing arrangements or structures. A variety of otherarrangements will be apparent to those of skill in the art.

2.7 Database Manipulation to Produce Useful Information

Databases formulated using the methods of the present invention areuseful in that they can be manipulated, for example, using a variety ofstatistical analyses, to produce useful information. The databases ofthe present invention may be generated, for example, from data collectedfor an individual or from a selected group of individuals over a definedperiod of time (e.g., hours, days, or months), from derived data, andfrom data attributes.

The present invention further relates to a method for manipulating ananalyte data points, derived data, and data attributes database in orderto provide a useful result, said method comprising providing an analytedata points, derived data, and data attributes database, andmanipulating and/or analyzing the database.

For example, data sets may be aggregated, sorted, selected, sifted,clustered and segregated by means of the attributes associated with thedata points. A number of database management systems and data miningsoftware programs exist which may be used to perform the desiredmanipulations.

Relationships in the database can be directly queried and/or the dataanalyzed by statistical methods to evaluate the information obtainedfrom manipulating the database.

For example, a distribution curve can be established for a selected dataset, and the mean, median and mode calculated therefor. Further, dataspread characteristics, e.g. variability, quartiles and standarddeviations can be calculated.

The nature of the relationship between a particular variable and analytelevels can be examined by calculating correlation coefficients. Usefulmethods for doing so include but are not limited to the following:Pearson Product Moment Correlation and Spearman Rank Order Correlation.

Analysis of variance permits testing of differences among sample groupsto determine whether a selected variable has a discernible effect on theparameter being measured. For example, the effect of various demographicfactors on the efficacy of an experimental drug in normalizing bloodglucose levels (efficacy being measured in terms of total glucoseexcursion) could be analyzed by performing an analysis of variancebetween two groups which differ only with respect to that particulardemographic factor.

Non-parametric tests may be used as a means of testing whethervariations between empirical data and experimental expectancies areattributable merely to chance or to the variable or variables beingexamined. These include the Chi Square test, the Chi Square Goodness ofFit, the 2×2 Contingency Table, the Sign Test, and the Phi CorrelationCoefficient.

There are numerous tools and analyses available in standard data miningsoftware that can be applied to the analysis of the databases of thepresent invention. Such tools and analyses include, but are not limitedto, cluster analysis, factor analysis, decision trees, neural networks,rule induction, data driven modeling, and data visualization. Some ofthe more complex methods of data mining techniques are used to discoverrelationships that are more empirical and data-driven, as opposed totheory-driven, relationships.

Exemplary data mining software that can be used in analysis and/orgeneration of the databases of the present invention includes, but isnot limited to: Link Analysis (e.g., Associations analysis, SequentialPatterns, Sequential time patterns and Bayes Networks); Classification(e.g., Neural Networks Classification, Bayesian Classification,k-nearest neighbors classification, linear discriminant analysis, Memorybased Reasoning, and Classification by Associations); Clustering (e.g.,k-Means Clustering, demographic clustering, relational analysis, andNeural Networks Clustering); Statistical methods (e.g., Means, Std dev,Frequencies, Linear Regression, non-linear regression, t-tests, F-test,Chi2 tests, Principal Component Analysis, and Factor Analysis);Prediction (e.g., Neural Networks Prediction Models, Radial BasedFunctions predictions, Fuzzy logic predictions, Times Series Analysis,and Memory based Reasoning); Operating Systems; and Others (e.g.,Parallel Scalability, Simple Query Language functions, and C++ objectsgenerated for applications). Companies that provide such softwareinclude, for example, the following: Adaptative Methods Group at UTS(UTS City Campus, Sydney, NSW 2000), CSI®, Inc., (Computer ScienceInnovations, Inc. Melbourne, Fla.), IBM® (International BusinessMachines Corporation, Armonk, N.Y.), Oracle® (Oracle Inc., RedwoodShores, Calif.) and SAS® (SAS Institute Inc., Cary, N.C.).

These methods and processes may be applied to the databases of thepresent invention, for example, databases comprising, analyte data sets,derived data, and data attributes.

For a general discussion of statistical methods applied to dataanalysis, see Applied Statistics for Science and Industry, by A. Romano,1977, Allyn and Bacon, publisher.

Some exemplary applications of the present invention are as follows.Databases generated using an analyte monitoring system, e.g. theGlucoWatch biographer, are useful in clinical studies examining drugefficacy in controlling blood glucose levels, and in examining a numberof variables which may affect drug efficacy. Further, such databases areuseful to individuals to help them understand trends in managing, forexample, diabetes. In addition, common risk factors can be identifiedamong selected groups. For example, a database including GlucoWatchbiographer data, user inputs, data identifiers, and subject identifiers,could be evaluated to identify whenever multiple hyperglycemic eventsoccurred within a pre-defined time period. Statistical analyses couldthen be applied to data attributes associated in the database with thesevalues. Significant associations are identified using appropriatestatistical methods and may, for example, identify that such multiplehyperglycemic events are most common during the night in men who did noteat a meal or snack before retiring.

Several examples of identifying important correlations among parametersin an analyte data points, derived data, and data attributes databasegenerated as described herein are presented in Example 1. For example,analysis of the database provided a correlation between lower averageskin temperature and hypoglycemic blood glucose levels (FIG. 4).Accordingly, the results demonstrate that temperature may very well bewarranted as a useful parameter to assist in the prediction ofhypoglycemia. Further, FIG. 5 shows the average skin conductivityreading for all the measurement cycles within each reference bloodglucose range. As can be seen in the figure, the trend was relativelyflat over the euglycemic and hypoglycemic ranges. However, the threehighest averages occured in the <40 mg/dL, 40-59 mg/dL, and 60-79 mg/dLranges (i.e., the hypoglycemic region). These results indicated acorrelation of a higher degree of perspiration with the hypoglycemicreadings. Finally, the data from FIG. 5 were analyzed in a differentmanner by plotting the percentage of all readings with skin conductivityreadings greater than one over the same reference blood glucose ranges(FIG. 6). As can be seen from analysis of the data in FIG. 6, there is apronounced increase in the percentage of positive perspirationindications in the hypoglycemic regions below 60 mg/dL.

2.8 Hardware/Software and System Considerations

A. Hardware/Software

Various computer systems, typically comprising one or moremicroprocessors, can be operably connected to the analyte monitoringsystem to store, retrieve, and analyze database information. Thecomputer system can be as simple as a stand-alone computer that is notnetworked to other computers, provided the system has a form of datastorage, for example disk drives, removable disk storage, for exampleZIP® drives (Iomega Corporation, Roy, Utah), optical medium (e.g.,CD-ROM), magnetic tape, solid-state memory, and/or bubble memory.Alternatively, the computer system can include a networked computersystem in which a computer is linked to one or more additionalcomputers, for example a network server. The networked system can be anintranet system and/or a system linked to other computers via theInternet. Thus, the computer systems can be Internet-based systems ornon-Internet based systems.

In addition, devices such as the Personal Digital Assistants (PDA), forexample Palm Pilot™ (Palm Inc., Santa Clara, Calif.) or Handspring™Visor™ (Handspring, Inc., Mountain View, Calif.) and Pocket PCs (PPC),for example Casio® EM500 Multimedia Cassiopeia Pocket PC (Casio Inc.,Dover, N.J.) or Compaq® iPAQ™ (Compaq Computer Corporation, Houston,Tex.) can be operably connected to the analyte monitoring system tostore and retrieve patient database information. The PDA or PPC can be asimple stand-alone device that is not networked to other computers,provided the device has a form of data storage, for example solid-statememory, SD (secure digital) and MMC (multimedia card) cards.Alternatively, the PDA or PPC can be attached to a network in which theunit is linked to one or more computers, for example a network server orPC. The networked PDA or PPC can be an intranet system and/or a systemlinked to computers via the Internet. Thus, the PDA or PPC systems canbe Internet attached systems or non-Internet attached systems.

B. Stand-Alone System

FIG. 7 is an illustration of a representative computer system, PDA orPPC suitable for performing the methods of the present invention;however FIG. 7 depicts but one example of many possible computer typesor configurations capable of being used with the present invention(e.g., the GlucoWatch biographer, a glucose monitoring device, used asan example of an analyte monitoring system). The following descriptionsof the glucose monitoring device (as an example of an analyte monitoringdevice) include descriptions of the GlucoWatch biographer (Cygnus, Inc.,Redwood City, Calif.) and a similar multi-component version of thisdevice (see, for example, WO 00/47109, published 17 Aug. 2000, hereinincorporated by reference). A multi-component device may, for example,comprise two separate portions: one that takes the raw data andtransmits it to a second readout device that processes the data,displays it to the user, has basic alarm functions, and serves as theconnection to further devices. The basic communications between thesensor and the readout device are sending data to the readout device andsending command's back to the sensor. (These two portions could becombined into a single device comprising all of the functions of the twoportions.) Such a readout device can have a number of embodiments. Itcan be a pager, a watch, a credit-card sized device, a personal digitalassistant, a cellular phone, or other device. Several devices canconnect to this readout device. The readout device can, for example,communicate to an auxiliary alarm device to provide an enhance alarm(e.g., a louder alarm sound, a synthesized voice alarm, or alarm remotefrom the readout device). The readout can be connected to a PC in orderto, for example, download data to a data analysis program. Thisconnection could be made directly, or via some type of serial interfaceadapter (as described below).

Connection from the readout device to a central network (e.g., theInternet) can be made either directly, or via serial interface adapter.For example, a direct connection could be made if the readout device hadwireless capability; alternately, a connection through a SIA or othersort of docking station between the device and the network.

FIG. 7 depicts a representative stand-alone computer system, PDA or PPC702 interfaced with a glucose monitor system 701. In some instances, acomputer system, PDA or PPC includes a computer having an Intel®Pentium® microprocessor (Intel Corporation, Santa Clara, Calif.) thatruns the Microsoft® WINDOWS® Version 3.1, WINDOWS95®, WINDOWS98®, orWINDOWS2000® operating system (Microsoft Corporation, Redmond, Wash.).Of course other microprocessors such as the ATHLON™ microprocessor(Advanced Micro Devices, Inc., Sunnyvale, Calif.) and the Intel®CELERON® and XEON® microprocessors can be utilized. The methods andsystems can also include other operating systems, for example, UNIX,LINUX, Apple MAC OS 9 and OS X (Apple, Cupertino, Calif.), PalmOS® (PalmInc., Santa Clara, Calif.), Windows® CE 2.0 or Windows® CE Professional(Microsoft Corporation, Redmond, Wash.) without departing from the scopeof the present invention. Also illustrated in FIG. 7 is the storagemedia 705, for example disk drive, removable disk storage, CD-ROM,required to store and retrieve patient database information. FIG. 7illustrates a secondary alarm 703 and pager 704 which can be used with aglucose monitoring device. An exemplary secondary alarm is one thatamplifies or transforms the alert/alarm of the analyte monitoring device(e.g., an alert/alarm triggered by an out of range glucose value, apredicted out of range glucose value, or change in glucose levels thatexceed a predetermined rate) to be more readily perceived by the user ofthe analyte monitoring device. Alternately, such a secondary alarm maybe placed near a significant other or parent to alert a person otherthan the user to the alert/alarm. A paging device (e.g., pager, cellphone, etc.) may be used to alert a person other than the user to thealert/alarm, for example, a health care professional or emergencymedical support (see, for example, WO 00/47109, published 17 Aug. 2000,herein incorporated by reference).

A glucose monitoring device communicates with a computer system, PDA orPPC using an standard computer interface, for example a serial interfaceor Universal Serial Bus (USB) port. Alternatively, a glucose monitoringdevice can communicate with the computer system, PDA or PPC using astandard wireless interface, for example radio frequency (RF)technology—IEEE 802.11 and Bluetooth, and/or infrared technologies. Thedata can be encoded in the standard manner, for example AmericanStandard Code for Information Interchange (ASCII) format—a standardseven-bit code that was proposed by ANSI in 1963, and finalized in 1968.ASCII is the common code for microcomputer equipment.

The computer system, PDA or PPC can store the information into adatabase using a wide variety of existing software which provides ameans for inputting data points, and associating the data points withdata attributes. Available systems for generating databases andmanipulating the resulting databases include but are not limited toExcel® (Microsoft® Corporation, Seattle, Wash.) spreadsheet software,Quattro® (Corel Inc., Ottawa, Canada), Sybase® (Sybase Systems,Emeryville, Calif.), Oracle® (Oracle Inc., Redwood Shores, Calif.), andSagent Design Studio® (Sagent Technologies Inc., Mountain View, Calif.)systems software. Further, statistical packages and systems for dataanalysis and data mining are also available (see above). Illustrativeexamples include but are not limited to SAS® (SAS Institute Inc., Cary,N.C.) and SPSS® (SPSS Inc., Chicago, Ill.). The database can be recordedon, for example a disk drive—internal or external to the system, aRead/Write CD-ROM drive, a tape storage system, solid-state memory orbubble memory, an SD or MMC. In addition to saving the data in adatabase, a glucose monitoring device and/or computer system, PDA or PPCcan forward the information to an auxiliary readout device such as asecondary alarm, pager, cellular phone, or other display monitor.

Alternatively, the computer system, PDA or PPC can communicate with aglucose monitoring device using the same wired or wireless interface,for example to update the software running on a glucose monitoringdevice or provide feedback to the user based upon an analysis run on thecomputer system, PDA or PPC which requires data not available on aglucose monitoring device.

C. Networked System

FIG. 8 is an illustration of a representative networked computer system,PDA or PPC suitable for performing the methods of the present invention;however FIG. 8 depicts but one example of many possible networkedcomputer types or configurations capable of being used with the presentinvention. FIG. 8 depicts a representative networked computer system,PDA or PPC 802 operably interfaced with a glucose monitoring device 801.The networked computer system, PDA or PPC can interface with a glucosemonitoring device in a similar manner as the stand-alone computersystem, PDA or PPC. Further illustrated in FIG. 8, the computer system,PDA or PPC can be connected to a number of network systems, for examplea local area network (LAN) or a wide area network (WAN) 803. The networkcomputer system, PDA or PPC includes the necessary functionality forforwarding the data in established formats, for example Ethernet orToken Ring Packets or Frames, HTML-formatted data, or WAN digital oranalog protocols, in combination with any parameter information, forexample Destination Address, or Cyclic Redundancy Check (CRC). CRC is apowerful and-easily implemented technique to obtain data reliability.The CRC technique is used to protect blocks of data called Frames. Usingthis technique, the transmitter appends an extra n-bit sequence to everyframe called Frame Check Sequence (FCS). The FCS holds redundantinformation about the frame that helps the transmitter detect errors inthe frame. The CRC is one of the most used techniques for errordetection in data communications into a format suitable for transmissionacross a transmission line for delivery to a database server. Oneexample of such a data integrity check is shown in Column 7 of FIG. 3(CHECK SUM). Further, the network system may comprises the necessarysoftware and hardware to receive the data from the readout device, storethe data, process the data, display the data in a variety of ways, andcommunicate back to the readout device as well as to allow communicationamong a variety of users and between these users to the readout device.

The networked computer system, PDA or PPC can be connected to, forexample an Ethernet, Token Ring or FDDI network, using a standardnetwork interface card (NIC), for example a 3Com® EtherLink® NIC (3Com,Inc, Santa Clara, Calif.) which provide network connections over UTP,coaxial, or fiber-optic cabling or an Intel® PRO/100 S Desktop Adapter(Intel Corporation, Santa Clara, Calif.). The networked computer system,PDA or PPC can be connected to a LAN using a standard remote accesstechnology, for example a modem using a plain old telephone system(POTS) line, or a xDSL router connected to a digital subscriber lines(DSL), or a cable modem. Additionally, the networked computer system,PDA or PPC can be connected to the LAN using a standard wirelessinterface, for example radio frequency (RF) technology—IEEE 802.11 andBluetooth.

The networked computer system, PDA or PPC would have the same capabilityof storing data, as the stand-alone system, from a glucose monitoringdevice onto a storage media, for example a disk drive, tape storage, orCD-ROM. Alternatively FIG. 8 illustrates, the networked computer system,PDA or PPC 802 would be able to transfer data to any device connected tothe networked computer system, PDA or PPC, for example a medical doctoror medical care facility using standard e-mail software 804, a centraldatabase using database query and update software 806 (e.g., a datawarehouse of data points, derived data, and data attributes obtainedfrom a large number of users using GlucoWatch biographer, where the datawarehouse is maintained by Cygnus, Inc., the manufacturer of thebiographer), and/or the user's personal database 805 (e.g., the personaldatabase of a user of a GlucoWatch biographer, designated in the figureas “My GlucoWatch Website”). Alternatively, the user 807 could accesstheir “My GlucoWatch Website” 805 from a doctor's office or medicalfacility, using any computer system with Internet access, to reviewhistorical data which may be useful for determining treatment. When aglucose monitoring device is connected to a networked computer system,PDA or PPC, it has the capability to store or retrieve data from anyappropriate database worldwide.

If the networked computer system, PDA or PPC on the LAN includes a WorldWide Web application, the application includes the executable coderequired to generate database language statements, for example, SQLstatements. Such executables typically include embedded SQL statements.The application further includes a configuration file that containspointers and addresses to the various software entities that are locatedon the database server in addition to the different external andinternal databases that are accessed in response to a user request. Theconfiguration file also directs requests for database server resourcesto the appropriate hardware, as may be necessary if the database serveris distributed over two or more different computers.

Usually each networked computer system, PDA or PPC includes a World WideWeb browser that provides a user interface to the networked databaseserver. The networked computer system, PDA or PPC is able to constructsearch requests for retrieving information from a database via a Webbrowser. With access to a Web browser users can typically point andclick to user interface elements such as buttons, pull down menus, andother graphical user interface elements to prepare and submit a querythat extracts the relevant information from the database. Requestsformulated in this manner are subsequently transmitted to the Webapplication that formats the requests to produce a query that can beused to extract the relevant information from the database.

When Web-based applications are utilized, the Web application accessesdata from a database by constructing a query in a database language suchas Sybase or Oracle SQL which is then transferred to a relationaldatabase management system that in turn processes the query to obtainthe pertinent information from the database.

Accordingly, in one aspect the present invention describes a method ofconnecting a glucose monitoring device to a computer, a PDA, a network,for example the Internet, and methods of using this connection toprovide real-time and delayed data analysis, alert functions, and deviceand troubleshooting and servicing functions. The central network canalso allow access by the physician to a subject's data. Similarly, analert could be sent to the physician if a subject's readings are out ofa predetermined range, etc. The physician can then send advice back tothe patient via e-mail or a message on a web page interface (e.g. “MyGlucoWatch”). Also, the central network can provide a way for themanufacturer of an analyte monitoring device to communicate to a user ofthe device and/or the device itself. For example, troubleshooting of theanalyte monitoring device can be facilitated by interrogating the devicethrough the network. Software updates can be downloaded into the devicefrom the manufacturer as well. Entering codes to ensure that a user hasbeen trained in correct usage of the device can also be done. Further,access to the entire database of data from all users of an analytemonitoring device may be useful to the manufacturer (and others) forstatistical or research purposes. Appropriate network security features(e.g., for data transfer, inquiries, device updates, etc.) are of courseemployed.

In addition, because the analyte monitoring device is uploading datacontinually, frequently, or periodically to the central network, nearreal-time feedback can be provided to a user in the form of alerts, forexample, if readings are outside of a predetermined range, or the deviceseems to be malfunctioning. Advanced trend analysis software can beincorporated into the central network that can activate alerts to theuser (e.g., if post-meal glucose readings are out of control). Delayedalerts or suggestions can also be delivered to the user daily or atfrequencies determined by the uploading frequency of the analytemonitoring device.

In the context of a glucose monitoring device providing frequent glucosemeasurements (e.g., the GlucoWatch biographer or similar device), oneadvantage of network conductivity is that the data can be streamed tothe network in real time and is sufficiently dense to allow decisions tobe made by software at the central network based on this data stream.Such decisions may be communicated to the user, as well as to otherpersons, such as a medical care professional. Decisions, such assuggestions on insulin dosage, caloric intake/output, etc., aredifficult to make accurately without such large amounts of data asprovided by the methods and devices of the present invention.

D. Graphical User Interface

In certain of the computer systems, an interface such as an interfacescreen that includes a suite of functions is included to enable users toeasily access the information they seek from the databases of theinvention. Such interfaces usually include a main menu page from which auser can initiate a variety of different types of analyses (such asdiscussed above, for example, initiate a search for hypoglycemic eventsand related attributes, followed by initiating a selected analysis toidentify salient factors). For example, the main menu page for thedatabases generally include buttons for accessing certain types ofinformation, including, but not limited to, project information,inter-project comparisons, times of day, events, dates, times, ranges ofanalyte values, etc.

An exemplary main user interface between the user of the analytemonitoring device (the subject) and the central network system may be apersonal, customized Web page (e.g., “My GlucoWatch” discussed above).This Web page can enable the patient to access all the data uploadedfrom the device to the central network and a variety of user-friendlyforms to allow the patient to analyze the data and draw usefulconclusions. The web page can also, for example, flag hypoglycemicevents, hyperglycemic events, etc.

E. Computer Program Products

A variety of computer program products can be utilized for conductingthe various methods and analyses disclosed herein. In general, thecomputer program products comprise a computer-readable medium and thecode necessary to perform the methods set forth supra. Thecomputer-readable medium on which the program instructions are encodedcan be any of a variety of known medium types, including, but notlimited to, microprocessors, floppy disks, hard drives, ZIP drives, WORMdrives, magnetic tape and optical medium such as CD-ROMs.

The following examples are put forth so as to provide those of ordinaryskill in the art with a complete disclosure and description of how tomake and use the devices, methods, and formulae of the presentinvention, and are not intended to limit the scope of what the inventorregards as the invention. Efforts have been made to ensure accuracy withrespect to numbers used (e.g., amounts, temperature, etc.) but someexperimental errors and deviations should be accounted for. Unlessindicated otherwise, parts are parts by weight, molecular weight isweight average molecular weight, temperature is in degrees Centigrade,and pressure is at or near atmospheric.

Experimental

Below are examples of specific embodiments for carrying out the presentinvention. The examples are offered for illustrative purposes only, andare not intended to limit the scope of the present invention in any way.

Efforts have been made to ensure accuracy with respect to numbers used(e.g., amounts, temperatures, etc.), but some experimental error anddeviation should, of course, be allowed for.

EXAMPLE 1 Temperature and Perspiration as Indicators of Hypoglycemia

Several researchers have investigated the correlation between skintemperature and perspiration and the presence of hypoglycemia. Bolinger,et al. (Bolinger, R. E., et al., Diabetes, 13, 600-605 (1964)) reportedthat a decrease in skin resistance, indicating the onset ofperspiration, coincided with the onset of hypoglycemic symptoms.Levandoski, et al. (Levandoski, L. A., et al., in Artificial Systems forInsulin Delivery, P. Brunetti, et al., Eds. Raven Press: New York(1983), p. 353-356) evaluated a then-commercially available device, theTeledyne Sleep Sentry® device which uses both skin conductivity andtemperature to detect nocturnal hypoglycemia. In a study ofinsulin-induced hypoglycemia in 27 diabetic patients, the devicecorrectly detected the hypoglycemia 80% of the time. In widespread use,however, the Sleep Sentry device demonstrated a high rate of falsepositive alarms, and was not a commercial success, and is not currentlyon the market.

Preliminary tests of the correlation between skin temperature and skinconductivity, and hypoglycemic blood glucose levels were performed ondata from one clinical trial. Temperature and perspiration data from theGlucoWatch biographer have been analyzed for a total of 213 GlucoWatchbiographer applications on 121 diabetic subjects. This data setconsisted of the temperature, perspiration measurement and referenceblood glucose value for 5346 GlucoWatch biographer measurement cycles.For this trial, the subjects were tested in a clinical setting, butallowed to walk around, etc, thus simulating a home environment.

In order to determine whether a correlation existed between skintemperature and perspiration, and hypoglycemia, the data were sortedinto reference blood glucose range bins from <40 mg/dL to 240 mg/dL. Theminimum skin temperature for each measurement cycle in each bin wasaveraged and plotted in FIG. 4. As can be seen, the skin temperature asmeasured by the GlucoWatch biographer was lower than average when thereference blood glucose was lower than 120 mg/dL, and was lowest whenthe blood glucose was in the lowest hypoglycemic range. This preliminaryresult demonstrated a correlation between lower average skin temperatureand hypoglycemic blood glucose levels. Accordingly, the resultsdemonstrate that temperature may very well be warranted as a usefulparameter to assist in the prediction of hypoglycemia.

The data from the skin conductivity sensor on the GlucoWatch biographerwas plotted in a similar manner. The GlucoWatch biogrpher skinconductivity measurements were converted to an arbitrary scale from0-10. For data integrity screening purposes, skin conductivity readingsabove 1 were considered an indication of perspiration occuring. FIG. 5shows the average skin conductivity reading for all the measurementcycles within each reference blood glucose range. The trend wasrelatively flat over the euglycemic and hyperglycemic ranges with thethree highest averages occuring in the <40 mg/dL, 40-59 mg/dL, and 60-79mg/dL ranges in the hypoglycemic region, indicating a higher degree ofperspiration in the hypoglycemic region.

These data were presented in a different manner (FIG. 6) by plotting thepercentage of all readings with skin conductivity readings greater thanone (therefore, above the a priori determined perspiration threshold)over the same reference blood glucose ranges. As can be seen from thedata, there is a pronounced increase in the percentage of positiveperspiration indications in the hypoglycemic regions below 60 mg/dL.

These analyses demonstrate some aspects of the usefulness of thedatabases and methods of the present invention.

Although preferred embodiments of the subject invention have beendescribed in some detail, it is understood that obvious variations canbe made without departing from the spirit and the scope of the inventionas defined by the appended claims.

1. A method of formulating one or more analyte data databases, saidmethod comprising; collecting analyte measurement values from one ormore subject using an analyte monitoring device, comprising a sensingdevice, for each subject; and said analyte monitoring device providing(i) frequent analyte measurement values, wherein said analytemeasurement values comprise acquired data points that are specificallyrelated to analyte amount or concentration in the subject, (ii) one ormore data attributes, and (iii) one or more error messages related toskipped analyte measurement values; and formulating said one or moreanalyte data databases by associating each of said data points and eachof said one or more error messages related to skipped analytemeasurement values with one or more data attributes.
 2. The method ofclaim 1, wherein said data points further comprise derived datadetermined from one or more acquired data points and the derived dataare associated with the data points from which they are derived.
 3. Themethod of claim 2, wherein each of said derived data are associated withone or more data attributes.
 4. The method of claim 1, wherein saidanalyte measurement values are collected from a single individual. 5.The method of claim 1, wherein said analyte measurement values arecollected from more than one individual.
 6. The method of claim 5,wherein said formulating further comprises compiling multiple databasesfrom each database where the data points are collected from a singleindividual and the data points for each single individual are associatedwith one or more relevant data attributes.
 7. The method of claim 1,wherein said analyte is a biological analyte.
 8. The method of claim 7,wherein said biological analyte is glucose.
 9. The method of claim 2,wherein said analyte is glucose and said derived data comprises glucoseamount or concentration.
 10. The method of claim 9, wherein said analytemonitoring device is a glucose monitoring device, said glucosemonitoring device comprising a sensing device, a display, and means toprovide an audible alert when glucose levels in a subject beingmonitored are outside of a predetermined range.
 11. The method of claim1, wherein said acquired data points comprise electrochemical signals.12. The method of claim 11, wherein said data attributes are selectedfrom the group consisting of: chronological information, userperspiration levels, device operating temperature, missed measurements;skipped measurements, user body temperature, user skin conductance,environmental variables, alarm events, activity codes, total excursion,mean value, statistical function, subject code, demographic information,physical characteristics, and disease-associated characteristics. 13.The method of claim 1, wherein said analyte monitoring device is capableof measuring more than one analyte.
 14. The method of claim 13, whereinone of said analytes is glucose.
 15. One or more analyte data databases,comprising data points collected using an analyte monitoring device,comprising a sensing device, wherein said analyte monitoring deviceprovides (i) frequent analyte measurement values, and said analytemeasurement values comprise data points that are specifically related toanalyte amount or concentration, and (ii) one or more data attributes;one or more data attributes; and one or more error messages relate toskipped analyte measurement values, wherein the data points and each ofsaid one or more error messages related to skipped analyte measurementvalues are associated with one or more relevant data attributes.
 16. Theone or more databases of claim 15, wherein said data points furthercomprise derived data determined from one or more acquired data pointsand the derived data are associated with the data points from which theyare derived.
 17. The one or more databases of claim 16, wherein each ofsaid derived data are associated with one or more data attributes. 18.The one or more databases of claim 15, wherein said analyte measurementvalues are collected from a single individual.
 19. The one or moredatabases of claim 15, wherein said analyte measurement values arecollected from more than one individual.
 20. (Canceled)
 21. The one ormore databases of claim 15, wherein said analyte is a biologicalanalyte.
 22. The one or more databases of claim 21, wherein saidbiological analyte is glucose.
 23. The one or more databases of claim16, wherein said analyte is glucose and said derived data comprisesglucose amount or concentration.
 24. (Canceled)
 25. The one or moredatabases of claim 15, wherein said acquired data points compriseelectrochemical signals.
 26. The one or more databases of claim 25,wherein said data attributes are selected from the group consisting of:chronological information, user perspiration levels, device operatingtemperature, missed measurements; skipped measurements, user bodytemperature, user skin conductance, environmental variables, alarmevents, activity codes, total excursion, mean value, statisticalfunction, subject code, demographic information, physicalcharacteristics, and disease-associated characteristics.
 27. The one ormore databases of claim 15, wherein said analyte measurement valuescomprise analyte measurement values for more than one analyte.
 28. Theone or more databases of claim 27, wherein one of said analytes isglucose.
 29. A method of manipulating one or more analyte datadatabases, comprising providing the one or more analyte data databasesof claim 15; and manipulating said data points via said attributesassociated with said data points to determine relationships between saiddata points and said attributes.
 30. A method of manipulating one ormore analyte data databases, comprising providing the one or moreanalyte data databases of claim 15; and manipulating said attributes viasaid data points associated with said attributes to determinerelationships between said attributes and said data points.
 31. Themethod of claim 1, wherein one or more of said analyte measurementvalues, one or more of said error messages, and one or more of said dataattributes are transferred to a server via a network.
 32. The method ofclaim 31, wherein said formulating is carried out on said server. 33.The method of claim 31, wherein said server communicates with saidanalyte monitoring device.
 34. The method of claim 29, wherein said oneor more analyte databases are located on a network database server. 35.The method of claim 34, wherein said manipulating is carried out on saidnetwork database server.
 36. The Method of claim 30, wherein said one ormore analyte databases are located on a network database server.
 37. Themethod of claim 36, wherein said manipulating is carried out on saidnetwork database server.
 38. The method of claim 1, wherein said one ormore data attributes associated with said data point or said skippedmeasurement value is one or more data attribute provided by the analytemonitoring device.
 39. The method of claim 38, wherein said one or moredata attributes are selected from the group consisting of chronologicalinformation, user perspiration level, device operating temperature userbody temperature, user skin conductance, environmental variable, numberof alarm events, and type of alarm events.
 40. The method of claim 1,wherein said one or more data attributes associated with said data pointor said skipped measurement value is one or more data attributecomprising a user input.
 41. The method of claim 40, wherein said one ormore data attributes are selected from the group consisting of activitycodes, sleep and administration of medications, dose of medications, andtimes of medications.
 42. The method of claim 1, wherein said one ormore data attributes associated with said data point or said skippedmeasurement value is one or more data identifier.
 43. The method ofclaim 43, wherein said one or more data identifiers is selected from thegroup consisting of maximum analyte values, minimum analyte values,hypoglycemic analyte values, and hyperglycemic analyte values.
 44. Themethod of claim 1, wherein said one or more data attributes associatedwith said data point or said skipped measurement value is one or moresubject identifier.
 45. The method of claim 44, wherein said one or moresubject identifiers is selected from the group consisting of a subjectcode, demographic information, physical characteristic, selected aspectsof the subject's medical history, and disease-associatedcharacteristics.
 46. The one or more databases of claim 15, wherein saidone or more data attributes associated with said data point or saidskipped measurement value is one or more data attribute provided by theanalyte monitoring device.
 47. The one or more databases of claim 46,wherein said one or more data attributes are selected from the groupconsisting of chronological information, user perspiration level, deviceoperating temperature, user body temperature, user skin conductance,environmental variable, number of alarm events, and type of alarmevents.
 48. The one or more databases of claim 15, wherein said one ormore data attributes associated with said data point or said skippedmeasurement value is one or more data attribute comprising a user input.49. The one or more databases of claim 48, wherein said one or more dataattributes are selected from the group consisting of activity codes,sleep and administration of medications, dose of medications, and timesof medications.
 50. The one or more databases of claim 15, wherein saidone or more data attributes associated with said data point or saidskipped measurement value is one or more data identifier.
 51. The one ormore databases of claim 50, wherein said one or more data identifiers isselected from the group consisting of maximum analyte values, minimumanalyte values, hypoglycemic analyte values, and hyperglycemic analytevalues.
 52. The one or more databases of claim 15, wherein said one ormore data attributes associated with said data point or said skippedmeasurement value is one or more subject identifier.
 53. The one or moredatabases of claim 52, wherein said one or more subject identifiers isselected from the group consisting of a subject code, demographicinformation, physical characteristic, selected aspects of the subject'smedical history, and disease-associated characteristics.